Here, we’re just setting a few options.
knitr::opts_chunk$set(
warning = TRUE, # show warnings during codebook generation
message = TRUE, # show messages during codebook generation
error = TRUE, # do not interrupt codebook generation in case of errors,
# usually better for debugging
echo = TRUE # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
Now, we’re preparing our data for the codebook.
library(codebook)
codebook_data <- rio::import("https://osf.io/s87kd/download", "csv")
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
# codebook_data <- rio::import("mydata.csv")
# omit the following lines, if your missing values are already properly labelled
codebook_data <- detect_missing(codebook_data,
only_labelled = TRUE, # only labelled values are autodetected as
# missing
negative_values_are_missing = FALSE, # negative values are missing values
ninety_nine_problems = TRUE, # 99/999 are missing values, if they
# are more than 5 MAD from the median
)
# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
codebook_data <- detect_scales(codebook_data)
## Warning in detect_scales(codebook_data): A items found, but no aggregate
## Warning in detect_scales(codebook_data): C items found, but no aggregate
## Warning in detect_scales(codebook_data): E items found, but no aggregate
## Warning in detect_scales(codebook_data): N items found, but no aggregate
## Warning in detect_scales(codebook_data): O items found, but no aggregate
library(labelled)
##
## Attaching package: 'labelled'
## The following object is masked from 'package:codebook':
##
## to_factor
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
dict <- rio::import("https://osf.io/cs678/download", "csv")
var_label(codebook_data) <- dict %>%
select(variable, label) %>%
dict_to_list()
val_labels(codebook_data$gender) <- c("male"= 1, "female"= 2)
val_labels(codebook_data$education) <- c("in high school"= 1,"finished high school"= 2,"some college"= 3,"college graduate"= 4,"graduate degree"= 5)
add_likert_labels <- function(x){
val_labels(x) <- c("Very Inaccurate"= 1,
"Moderately Inaccurate"= 2,
"Slightly Inaccurate"= 3,
"Slightly Accurate"= 4,
"Moderately Accurate"= 5,
"Very Accurate"= 6)
x
}
likert_items <- dict %>%
filter(Big6 !="") %>%
pull(variable)
codebook_data <- codebook_data %>%
mutate_at(likert_items, add_likert_labels)
reversed_items <- dict %>%
filter (Keying == -1) %>%
pull(variable)
codebook_data <- codebook_data %>%
rename_at(reversed_items, add_R)
codebook_data <- codebook_data %>%
mutate_at(vars(matches("\\dR$")),reverse_labelled_values)
codebook_data$extraversion <- codebook_data %>%
select(E1R:E5) %>%
aggregate_and_document_scale()
metadata(codebook_data)$name <- "25 Personality items representing 5 factors"
metadata(codebook_data)$description <- "25 personality self report items taken from the International Personality Item Pool (ipip.ori.org)"
metadata(codebook_data)$creator <- "William Revelle"
metadata(codebook_data)$citation <- "Revelle, W., Wilt, J., & Rosenthal, A. (2010). Individual differences in cognition: New methods for examining the personality-cognition link. In A. Gruszka, G. Matthews, & B. Szymura (Eds.), Handbook of individual differences in cognition: Attention, memory, and executive control (pp. 27–49). New York, NY: Springer."
metadata(codebook_data)$url <- "https://CRAN.R-project.org/package=psych"
metadata(codebook_data)$datePublished <- "2010-01-01"
metadata(codebook_data)$ temporalCoverage <- "Spring 2010"
metadata(codebook_data)$ spatialCoverage <- "Online"
Create codebook
codebook(codebook_data)
## Warning in doTryCatch(return(expr), name, parentenv, handler): Reliability CIs
## could not be computed for extraversion
## Warning in doTryCatch(return(expr), name, parentenv, handler): Package "ufs"
## needed to compute reliabilites.
## Warning in value[[3L]](cond): Reliability could not be computed for extraversion
## Warning in value[[3L]](cond): Package "ufs" needed to compute reliabilites.
Dataset name: 25 Personality items representing 5 factors
25 personality self report items taken from the International Personality Item Pool (ipip.ori.org)
Temporal Coverage: Spring 2010
Spatial Coverage: Online
Citation: Revelle, W., Wilt, J., & Rosenthal, A. (2010). Individual differences in cognition: New methods for examining the personality-cognition link. In A. Gruszka, G. Matthews, & B. Szymura (Eds.), Handbook of individual differences in cognition: Attention, memory, and executive control (pp. 27–49). New York, NY: Springer.
Date published: 2010-01-01
Creator:
| name | value |
|---|---|
| 1 | William Revelle |
|
#Variables
Am indifferent to the feelings of others.
Distribution of values for A1R
16 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| A1R | Am indifferent to the feelings of others. | haven_labelled | 16 | 0.9942857 | 1 | 5 | 6 | 4.586566 | 1.407737 | 6 | ▁▂▁▃▃▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Inquire about others’ well-being.
Distribution of values for A2
27 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| A2 | Inquire about others’ well-being. | haven_labelled | 27 | 0.9903571 | 1 | 5 | 6 | 4.80238 | 1.17202 | 6 | ▁▁▁▁▅▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Know how to comfort others.
Distribution of values for A3
26 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| A3 | Know how to comfort others. | haven_labelled | 26 | 0.9907143 | 1 | 5 | 6 | 4.603821 | 1.301834 | 6 | ▁▂▁▂▅▁▇▆ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Love children.
Distribution of values for A4
19 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| A4 | Love children. | haven_labelled | 19 | 0.9932143 | 1 | 5 | 6 | 4.699748 | 1.479633 | 6 | ▁▂▁▁▃▁▅▇ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Make people feel at ease.
Distribution of values for A5
16 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| A5 | Make people feel at ease. | haven_labelled | 16 | 0.9942857 | 1 | 5 | 6 | 4.560345 | 1.258512 | 6 | ▁▂▁▂▅▁▇▆ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Am exacting in my work.
Distribution of values for C1
21 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| C1 | Am exacting in my work. | haven_labelled | 21 | 0.9925 | 1 | 5 | 6 | 4.502339 | 1.241346 | 6 | ▁▁▁▂▅▁▇▅ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Continue until everything is perfect.
Distribution of values for C2
24 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| C2 | Continue until everything is perfect. | haven_labelled | 24 | 0.9914286 | 1 | 5 | 6 | 4.369957 | 1.318347 | 6 | ▁▂▁▂▆▁▇▅ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Do things according to a plan.
Distribution of values for C3
20 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| C3 | Do things according to a plan. | haven_labelled | 20 | 0.9928571 | 1 | 5 | 6 | 4.303957 | 1.288552 | 6 | ▁▂▁▂▆▁▇▅ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Do things in a half-way manner.
Distribution of values for C4R
26 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| C4R | Do things in a half-way manner. | haven_labelled | 26 | 0.9907143 | 1 | 5 | 6 | 4.446647 | 1.375118 | 6 | ▁▂▁▅▅▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Waste my time.
Distribution of values for C5R
16 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| C5R | Waste my time. | haven_labelled | 16 | 0.9942857 | 1 | 4 | 6 | 3.703305 | 1.628542 | 6 | ▃▆▁▇▅▁▇▆ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Get angry easily.
Distribution of values for N1R
22 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| N1R | Get angry easily. | haven_labelled | 22 | 0.9921429 | 1 | 4 | 6 | 4.070914 | 1.570917 | 6 | ▂▅▁▆▅▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Get irritated easily.
Distribution of values for N2R
21 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| N2R | Get irritated easily. | haven_labelled | 21 | 0.9925 | 1 | 3 | 6 | 3.492263 | 1.525944 | 6 | ▃▆▁▇▅▁▆▃ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Have frequent mood swings.
Distribution of values for N3R
11 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| N3R | Have frequent mood swings. | haven_labelled | 11 | 0.9960714 | 1 | 4 | 6 | 3.783435 | 1.602902 | 6 | ▃▆▁▇▅▁▇▆ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Often feel blue.
Distribution of values for N4R
36 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| N4R | Often feel blue. | haven_labelled | 36 | 0.9871429 | 1 | 4 | 6 | 3.814399 | 1.569685 | 6 | ▃▅▁▇▅▁▇▆ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Panic easily.
Distribution of values for N5R
29 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| N5R | Panic easily. | haven_labelled | 29 | 0.9896429 | 1 | 4 | 6 | 4.030314 | 1.618647 | 6 | ▃▃▁▆▅▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Am full of ideas.
Distribution of values for O1
22 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| O1 | Am full of ideas. | haven_labelled | 22 | 0.9921429 | 1 | 5 | 6 | 4.816055 | 1.12953 | 6 | ▁▁▁▂▅▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Avoid difficult reading material.
Distribution of values for O2R
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| O2R | Avoid difficult reading material. | haven_labelled | 0 | 1 | 1 | 5 | 6 | 4.286786 | 1.565152 | 6 | ▂▃▁▅▃▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
Carry the conversation to a higher level.
Distribution of values for O3
28 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| O3 | Carry the conversation to a higher level. | haven_labelled | 28 | 0.99 | 1 | 5 | 6 | 4.438312 | 1.220901 | 6 | ▁▁▁▂▆▁▇▅ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Spend time reflecting on things.
Distribution of values for O4
14 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| O4 | Spend time reflecting on things. | haven_labelled | 14 | 0.995 | 1 | 5 | 6 | 4.892319 | 1.22125 | 6 | ▁▁▁▁▃▁▆▇ |
| name | value |
|---|---|
| Very Inaccurate | 1 |
| Moderately Inaccurate | 2 |
| Slightly Inaccurate | 3 |
| Slightly Accurate | 4 |
| Moderately Accurate | 5 |
| Very Accurate | 6 |
Will not probe deeply into a subject.
Distribution of values for O5R
20 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| O5R | Will not probe deeply into a subject. | haven_labelled | 20 | 0.9928571 | 1 | 5 | 6 | 4.510432 | 1.327959 | 6 | ▁▂▁▃▅▁▇▇ |
| name | value |
|---|---|
| Very Inaccurate | 6 |
| Moderately Inaccurate | 5 |
| Slightly Inaccurate | 4 |
| Slightly Accurate | 3 |
| Moderately Accurate | 2 |
| Very Accurate | 1 |
gender
Distribution of values for gender
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| gender | gender | haven_labelled | 0 | 1 | 1 | 2 | 2 | 1.671786 | 0.4696471 | 2 | ▃▁▁▁▁▁▁▇ |
| name | value |
|---|---|
| male | 1 |
| female | 2 |
education
Distribution of values for education
223 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|
| education | education | haven_labelled | 223 | 0.9203571 | 1 | 3 | 5 | 3.190144 | 1.107714 | 5 | ▂▂▁▇▁▂▁▃ |
| name | value |
|---|---|
| in high school | 1 |
| finished high school | 2 |
| some college | 3 |
| college graduate | 4 |
| graduate degree | 5 |
age
Distribution of values for age
0 missing values.
| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| age | age | numeric | 0 | 1 | 3 | 26 | 86 | 28.78214 | 11.12755 | ▃▇▂▁▁ |
Reliability: Not computed.
Missing: 87.
Likert plot of scale extraversion items
Distribution of scale extraversion
| name | label | data_type | value_labels | n_missing | complete_rate | min | median | max | mean | sd | n_value_labels | hist |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| E1R | Don’t talk a lot. | haven_labelled | 6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
23 | 0.9917857 | 1 | 4 | 6 | 4.025567 | 1.631506 | 6 | ▃▅▁▆▅▁▇▇ |
| E2R | Find it difficult to approach others. | haven_labelled | 6. Very Inaccurate, 5. Moderately Inaccurate, 4. Slightly Inaccurate, 3. Slightly Accurate, 2. Moderately Accurate, 1. Very Accurate |
16 | 0.9942857 | 1 | 4 | 6 | 3.858118 | 1.605210 | 6 | ▃▅▁▇▅▁▇▆ |
| E3 | Know how to captivate people. | haven_labelled | 1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
25 | 0.9910714 | 1 | 4 | 6 | 4.000721 | 1.352719 | 6 | ▂▃▁▃▇▁▇▃ |
| E4 | Make friends easily. | haven_labelled | 1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
9 | 0.9967857 | 1 | 5 | 6 | 4.422429 | 1.457517 | 6 | ▁▂▁▂▃▁▇▆ |
| E5 | Take charge. | haven_labelled | 1. Very Inaccurate, 2. Moderately Inaccurate, 3. Slightly Inaccurate, 4. Slightly Accurate, 5. Moderately Accurate, 6. Very Accurate |
21 | 0.9925000 | 1 | 5 | 6 | 4.416337 | 1.334768 | 6 | ▁▂▁▂▅▁▇▅ |
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "25 Personality items representing 5 factors",
"description": "25 personality self report items taken from the International Personality Item Pool (ipip.ori.org)\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name |label | n_missing|\n|:------------|:-----------------------------------------|---------:|\n|A1R |Am indifferent to the feelings of others. | 16|\n|A2 |Inquire about others' well-being. | 27|\n|A3 |Know how to comfort others. | 26|\n|A4 |Love children. | 19|\n|A5 |Make people feel at ease. | 16|\n|C1 |Am exacting in my work. | 21|\n|C2 |Continue until everything is perfect. | 24|\n|C3 |Do things according to a plan. | 20|\n|C4R |Do things in a half-way manner. | 26|\n|C5R |Waste my time. | 16|\n|E1R |Don't talk a lot. | 23|\n|E2R |Find it difficult to approach others. | 16|\n|E3 |Know how to captivate people. | 25|\n|E4 |Make friends easily. | 9|\n|E5 |Take charge. | 21|\n|N1R |Get angry easily. | 22|\n|N2R |Get irritated easily. | 21|\n|N3R |Have frequent mood swings. | 11|\n|N4R |Often feel blue. | 36|\n|N5R |Panic easily. | 29|\n|O1 |Am full of ideas. | 22|\n|O2R |Avoid difficult reading material. | 0|\n|O3 |Carry the conversation to a higher level. | 28|\n|O4 |Spend time reflecting on things. | 14|\n|O5R |Will not probe deeply into a subject. | 20|\n|gender |gender | 0|\n|education |education | 223|\n|age |age | 0|\n|extraversion |5 E items aggregated by rowMeans | 87|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
"creator": "William Revelle",
"citation": "Revelle, W., Wilt, J., & Rosenthal, A. (2010). Individual differences in cognition: New methods for examining the personality-cognition link. In A. Gruszka, G. Matthews, & B. Szymura (Eds.), Handbook of individual differences in cognition: Attention, memory, and executive control (pp. 27–49). New York, NY: Springer.",
"url": "https://CRAN.R-project.org/package=psych",
"datePublished": "2010-01-01",
"temporalCoverage": "Spring 2010",
"spatialCoverage": "Online",
"keywords": ["A1R", "A2", "A3", "A4", "A5", "C1", "C2", "C3", "C4R", "C5R", "E1R", "E2R", "E3", "E4", "E5", "N1R", "N2R", "N3R", "N4R", "N5R", "O1", "O2R", "O3", "O4", "O5R", "gender", "education", "age", "extraversion"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "A1R",
"description": "Am indifferent to the feelings of others.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "A2",
"description": "Inquire about others' well-being.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "A3",
"description": "Know how to comfort others.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "A4",
"description": "Love children.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "A5",
"description": "Make people feel at ease.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "C1",
"description": "Am exacting in my work.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "C2",
"description": "Continue until everything is perfect.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "C3",
"description": "Do things according to a plan.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "C4R",
"description": "Do things in a half-way manner.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "C5R",
"description": "Waste my time.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "E1R",
"description": "Don't talk a lot.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "E2R",
"description": "Find it difficult to approach others.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "E3",
"description": "Know how to captivate people.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "E4",
"description": "Make friends easily.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "E5",
"description": "Take charge.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "N1R",
"description": "Get angry easily.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "N2R",
"description": "Get irritated easily.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "N3R",
"description": "Have frequent mood swings.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "N4R",
"description": "Often feel blue.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "N5R",
"description": "Panic easily.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "O1",
"description": "Am full of ideas.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "O2R",
"description": "Avoid difficult reading material.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "O3",
"description": "Carry the conversation to a higher level.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "O4",
"description": "Spend time reflecting on things.",
"value": "1. Very Inaccurate,\n2. Moderately Inaccurate,\n3. Slightly Inaccurate,\n4. Slightly Accurate,\n5. Moderately Accurate,\n6. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "O5R",
"description": "Will not probe deeply into a subject.",
"value": "6. Very Inaccurate,\n5. Moderately Inaccurate,\n4. Slightly Inaccurate,\n3. Slightly Accurate,\n2. Moderately Accurate,\n1. Very Accurate",
"maxValue": 6,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "gender",
"description": "gender",
"value": "1. male,\n2. female",
"maxValue": 2,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "education",
"description": "education",
"value": "1. in high school,\n2. finished high school,\n3. some college,\n4. college graduate,\n5. graduate degree",
"maxValue": 5,
"minValue": 1,
"@type": "propertyValue"
},
{
"name": "age",
"description": "age",
"@type": "propertyValue"
},
{
"name": "extraversion",
"description": "5 E items aggregated by rowMeans",
"@type": "propertyValue"
}
]
}`